{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\Anaconda3\\envs\\machine_learning\\lib\\site-packages\\tqdm\\auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using cuda:0\n"
     ]
    }
   ],
   "source": [
    "import random\n",
    "\n",
    "import torch\n",
    "from torchvision.transforms import Resize\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from Utils.utils import *\n",
    "from Utils.dataset import *\n",
    "from Utils.model_loading import *\n",
    "\n",
    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
    "print(f\"Using {device}\")\n",
    "\n",
    "random.seed(0)\n",
    "np.random.seed(0)\n",
    "torch.manual_seed(0)\n",
    "\n",
    "sns.set_style(\"whitegrid\")\n",
    "sns.set_palette(\"husl\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Benchmarks\n",
    "Evaluate all models and store their stats (accuracy, latency, ...) in a json file. Then load the stats from that json to use for comparisons.\n",
    "\n",
    "Note: Some benchmarks need to be updated. Some of the plotting functions were refactored and have not been updated here."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Benchmark All Models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- TODO: Test inference difference between TensorRT w/wo PyTorch loaded."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO]: Benchmarking model AlexNet\n",
      "[INFO]: \tGetting model params and MACs\n",
      "[INFO]: \tMeasuring latency\n",
      "[INFO]: \tEvaluating\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                       \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO]: Benchmarking for AlexNet finished\n",
      "[INFO]: Adding model stat to model_stats.json\n",
      "[INFO]: Benchmarking model VGG16\n",
      "[INFO]: \tGetting model params and MACs\n",
      "[INFO]: \tMeasuring latency\n",
      "[INFO]: \tEvaluating\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                       \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO]: Benchmarking for VGG16 finished\n",
      "[INFO]: Adding model stat to model_stats.json\n",
      "[INFO]: Benchmarking model ResNet18\n",
      "[INFO]: \tGetting model params and MACs\n",
      "[INFO]: \tMeasuring latency\n",
      "[INFO]: \tEvaluating\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                       \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO]: Benchmarking for ResNet18 finished\n",
      "[INFO]: Adding model stat to model_stats.json\n",
      "[INFO]: Benchmarking model MobileNetV3 Large\n",
      "[INFO]: \tGetting model params and MACs\n",
      "[INFO]: \tMeasuring latency\n",
      "[INFO]: \tEvaluating\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                       \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO]: Benchmarking for MobileNetV3 Large finished\n",
      "[INFO]: Adding model stat to model_stats.json\n",
      "[INFO]: Benchmarking model MobileNetV3 Small\n",
      "[INFO]: \tGetting model params and MACs\n",
      "[INFO]: \tMeasuring latency\n",
      "[INFO]: \tEvaluating\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                       \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO]: Benchmarking for MobileNetV3 Small finished\n",
      "[INFO]: Adding model stat to model_stats.json\n",
      "[INFO]: Benchmarking model OFA 595M\n",
      "[INFO]: \tGetting model params and MACs\n",
      "[INFO]: \tMeasuring latency\n",
      "[INFO]: \tEvaluating\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                       \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO]: Benchmarking for OFA 595M finished\n",
      "[INFO]: Adding model stat to model_stats.json\n",
      "[INFO]: Benchmarking model OFA Pixel 1 20ms\n",
      "[INFO]: \tGetting model params and MACs\n",
      "[INFO]: \tMeasuring latency\n",
      "[INFO]: \tEvaluating\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                       \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO]: Benchmarking for OFA Pixel 1 20ms finished\n",
      "[INFO]: Adding model stat to model_stats.json\n",
      "[INFO]: Benchmarking model MobileNetV3 Small - Resolution 80\n",
      "[INFO]: \tGetting model params and MACs\n",
      "[INFO]: \tMeasuring latency\n",
      "[INFO]: \tEvaluating\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                       \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO]: Benchmarking for MobileNetV3 Small - Resolution 80 finished\n",
      "[INFO]: Adding model stat to model_stats.json\n",
      "[INFO]: Benchmarking model MobileNetV3 Small - Resolution 104\n",
      "[INFO]: \tGetting model params and MACs\n",
      "[INFO]: \tMeasuring latency\n",
      "[INFO]: \tEvaluating\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                       \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO]: Benchmarking for MobileNetV3 Small - Resolution 104 finished\n",
      "[INFO]: Adding model stat to model_stats.json\n",
      "[INFO]: Benchmarking model MobileNetV3 Small - Resolution 128\n",
      "[INFO]: \tGetting model params and MACs\n",
      "[INFO]: \tMeasuring latency\n",
      "[INFO]: \tEvaluating\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                       \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO]: Benchmarking for MobileNetV3 Small - Resolution 128 finished\n",
      "[INFO]: Adding model stat to model_stats.json\n",
      "[INFO]: Benchmarking model MobileNetV3 Small - Resolution 152\n",
      "[INFO]: \tGetting model params and MACs\n",
      "[INFO]: \tMeasuring latency\n",
      "[INFO]: \tEvaluating\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                       \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO]: Benchmarking for MobileNetV3 Small - Resolution 152 finished\n",
      "[INFO]: Adding model stat to model_stats.json\n",
      "[INFO]: Benchmarking model MobileNetV3 Small - Resolution 176\n",
      "[INFO]: \tGetting model params and MACs\n",
      "[INFO]: \tMeasuring latency\n",
      "[INFO]: \tEvaluating\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                       \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO]: Benchmarking for MobileNetV3 Small - Resolution 176 finished\n",
      "[INFO]: Adding model stat to model_stats.json\n",
      "[INFO]: Benchmarking model MobileNetV3 Small - Resolution 200\n",
      "[INFO]: \tGetting model params and MACs\n",
      "[INFO]: \tMeasuring latency\n",
      "[INFO]: \tEvaluating\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                       \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO]: Benchmarking for MobileNetV3 Small - Resolution 200 finished\n",
      "[INFO]: Adding model stat to model_stats.json\n",
      "[INFO]: Benchmarking model MobileNetV3 Small - Resolution 224\n",
      "[INFO]: \tGetting model params and MACs\n",
      "[INFO]: \tMeasuring latency\n",
      "[INFO]: \tEvaluating\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                       \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO]: Benchmarking for MobileNetV3 Small - Resolution 224 finished\n",
      "[INFO]: Adding model stat to model_stats.json\n",
      "[INFO]: Benchmarking model VGG16 - Layer Pruned 0.5\n",
      "[INFO]: \tGetting model params and MACs\n",
      "[INFO]: \tMeasuring latency\n",
      "[INFO]: \tEvaluating\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                       \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO]: Benchmarking for VGG16 - Layer Pruned 0.5 finished\n",
      "[INFO]: Adding model stat to model_stats.json\n",
      "[INFO]: Benchmarking model VGG16 - Global Pruned 0.5\n",
      "[INFO]: \tGetting model params and MACs\n",
      "[INFO]: \tMeasuring latency\n",
      "[INFO]: \tEvaluating\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                       \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO]: Benchmarking for VGG16 - Global Pruned 0.5 finished\n",
      "[INFO]: Adding model stat to model_stats.json\n",
      "[INFO]: Benchmarking model VGG16 - Layer Pruned 0.6\n",
      "[INFO]: \tGetting model params and MACs\n",
      "[INFO]: \tMeasuring latency\n",
      "[INFO]: \tEvaluating\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                       \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO]: Benchmarking for VGG16 - Layer Pruned 0.6 finished\n",
      "[INFO]: Adding model stat to model_stats.json\n",
      "[INFO]: Benchmarking model VGG16 - Global Pruned 0.6\n",
      "[INFO]: \tGetting model params and MACs\n",
      "[INFO]: \tMeasuring latency\n",
      "[INFO]: \tEvaluating\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                       \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO]: Benchmarking for VGG16 - Global Pruned 0.6 finished\n",
      "[INFO]: Adding model stat to model_stats.json\n",
      "[INFO]: Benchmarking model VGG16 - Layer Pruned 0.7\n",
      "[INFO]: \tGetting model params and MACs\n",
      "[INFO]: \tMeasuring latency\n",
      "[INFO]: \tEvaluating\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                       \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO]: Benchmarking for VGG16 - Layer Pruned 0.7 finished\n",
      "[INFO]: Adding model stat to model_stats.json\n",
      "[INFO]: Benchmarking model VGG16 - Global Pruned 0.7\n",
      "[INFO]: \tGetting model params and MACs\n",
      "[INFO]: \tMeasuring latency\n",
      "[INFO]: \tEvaluating\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                       \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO]: Benchmarking for VGG16 - Global Pruned 0.7 finished\n",
      "[INFO]: Adding model stat to model_stats.json\n",
      "[INFO]: Benchmarking model VGG16 - Layer Pruned 0.8\n",
      "[INFO]: \tGetting model params and MACs\n",
      "[INFO]: \tMeasuring latency\n",
      "[INFO]: \tEvaluating\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                       \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO]: Benchmarking for VGG16 - Layer Pruned 0.8 finished\n",
      "[INFO]: Adding model stat to model_stats.json\n",
      "[INFO]: Benchmarking model VGG16 - Global Pruned 0.8\n",
      "[INFO]: \tGetting model params and MACs\n",
      "[INFO]: \tMeasuring latency\n",
      "[INFO]: \tEvaluating\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                       \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO]: Benchmarking for VGG16 - Global Pruned 0.8 finished\n",
      "[INFO]: Adding model stat to model_stats.json\n",
      "[INFO]: Benchmarking model VGG16 - Layer Pruned 0.9\n",
      "[INFO]: \tGetting model params and MACs\n",
      "[INFO]: \tMeasuring latency\n",
      "[INFO]: \tEvaluating\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                       \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO]: Benchmarking for VGG16 - Layer Pruned 0.9 finished\n",
      "[INFO]: Adding model stat to model_stats.json\n",
      "[INFO]: Benchmarking model VGG16 - Global Pruned 0.9\n",
      "[INFO]: \tGetting model params and MACs\n",
      "[INFO]: \tMeasuring latency\n",
      "[INFO]: \tEvaluating\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                       "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO]: Benchmarking for VGG16 - Global Pruned 0.9 finished\n",
      "[INFO]: Adding model stat to model_stats.json\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r"
     ]
    }
   ],
   "source": [
    "json_path = \"model_stats.json\"\n",
    "\n",
    "# Main models\n",
    "dataloader = build_dataloader(\n",
    "    train_path = \"Data/mitbih_mif_train_small.h5\",\n",
    "    test_path  = \"Data/mitbih_mif_test.h5\",\n",
    "    batch_size = 128,\n",
    "    transform  = Resize((152, 152), antialias=None)\n",
    ")\n",
    "\n",
    "models = {\n",
    "    \"alexnet\":            \"AlexNet\",\n",
    "    \"vgg16_bn\":           \"VGG16\",\n",
    "    \"vgg16_bn_custom\":    \"VGG16 Custom 152\",\n",
    "    \"resnet18\":           \"ResNet18\", \n",
    "    \"mobilenet_v3\":       \"MobileNetV3 Large\", \n",
    "    \"mobilenet_v3_small\": \"MobileNetV3 Small\", \n",
    "    \"ofa_595M\":           \"OFA 595M\", \n",
    "    \"ofa_pixel1_20\":      \"OFA Pixel 1 20ms\",\n",
    "}\n",
    "\n",
    "for model, name in models.items():\n",
    "    loaded_model = load_model_from_pretrained(model, f\"Pretrained/{model}_ecg_ep50_i152.pth\", 5)\n",
    "    stat = benchmark_model(loaded_model, dataloader[\"test\"], name)\n",
    "    add_model_stat_to_json(json_path, stat)\n",
    "    del loaded_model\n",
    "\n",
    "# MobileNetV3 Small resolution analysis models\n",
    "resolutions = [80, 104, 128, 152, 176, 200, 224]\n",
    "for r in resolutions:\n",
    "    dataloader = build_dataloader(\n",
    "        train_path = \"Data/mitbih_mif_train_small.h5\",\n",
    "        test_path  = \"Data/mitbih_mif_test.h5\",\n",
    "        batch_size = 128,\n",
    "        transform  = Resize((r, r), antialias=None)\n",
    "    )\n",
    "    model = load_model_from_pretrained(\"mobilenet_v3_small\", f\"Pretrained/MobileNetV3-Small/mobilenet_v3_small_ecg_ep15_i{r}.pth\", 5)\n",
    "    stat = benchmark_model(model, dataloader[\"test\"], f\"MobileNetV3 Small - Resolution {r}\")\n",
    "    add_model_stat_to_json(json_path, stat)\n",
    "    del model\n",
    "\n",
    "# Pruning\n",
    "dataloader = build_dataloader(\n",
    "    train_path = \"Data/mitbih_mif_train_small.h5\",\n",
    "    test_path  = \"Data/mitbih_mif_test.h5\",\n",
    "    batch_size = 32,\n",
    "    transform  = Resize((152, 152), antialias=None)\n",
    ")\n",
    "\n",
    "pruning_ratios = [0.5, 0.6, 0.7, 0.8, 0.9]\n",
    "for pr in pruning_ratios:\n",
    "    p_model = load_vgg_from_pruned(f\"Pretrained/VGG-Pruned/vgg16_bn_ecg_ep20_i152_p{pr}.pth\", pr, torch.rand((1, 3, 152, 152)))\n",
    "    p_stat = benchmark_model(p_model, dataloader[\"test\"], f\"VGG16 - Layer Pruned {pr}\")\n",
    "    add_model_stat_to_json(json_path, p_stat)\n",
    "    del p_model\n",
    "\n",
    "    g_model = torch.load(f\"Pretrained/VGG-Pruned/vgg16_bn_ecg_ep20_i152_g{pr}.pth\")\n",
    "    g_stat = benchmark_model(g_model, dataloader[\"test\"], f\"VGG16 - Global Pruned {pr}\")\n",
    "    add_model_stat_to_json(json_path, g_stat)\n",
    "    del g_model\n",
    "\n",
    "# Pruned VGG custom\n",
    "p_model = load_vgg_custom_from_pruned(f\"Pretrained/VGG-Pruned/vgg16_bn_custom_ecg_ep30_i152_p0.8.pth\", 0.8, torch.rand((1, 3, 152, 152)))\n",
    "p_stat = benchmark_model(p_model, dataloader[\"test\"], f\"VGG16 Custom 152 - Layer Pruned 0.8\")\n",
    "add_model_stat_to_json(json_path, p_stat)\n",
    "del p_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[I]: Benchmarking TensorRT model ResNet18 - TRT FP32 from Quantization/Engine/resnet18_ecg_ep50_i152_fp32.engine\n",
      "[I]: \tEvaluating\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                            \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[I]: \tMeasuring latency\n",
      "[I]: Benchmark for ResNet18 - TRT FP32 finished\n",
      "[I]: Adding model stat to model_stats.json\n",
      "[I]: Benchmarking TensorRT model ResNet18 - TRT FP16 from Quantization/Engine/resnet18_ecg_ep50_i152_fp16.engine\n",
      "[I]: \tEvaluating\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                             \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[I]: \tMeasuring latency\n",
      "[I]: Benchmark for ResNet18 - TRT FP16 finished\n",
      "[I]: Adding model stat to model_stats.json\n",
      "[I]: Benchmarking TensorRT model MobileNetV3 Small - TRT FP32 from Quantization/Engine/mobilenet_v3_small_ecg_ep50_i152_fp32.engine\n",
      "[I]: \tEvaluating\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                            \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[I]: \tMeasuring latency\n",
      "[I]: Benchmark for MobileNetV3 Small - TRT FP32 finished\n",
      "[I]: Adding model stat to model_stats.json\n",
      "[I]: Benchmarking TensorRT model MobileNetV3 Small - TRT FP16 from Quantization/Engine/mobilenet_v3_small_ecg_ep50_i152_fp16.engine\n",
      "[I]: \tEvaluating\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                            \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[I]: \tMeasuring latency\n",
      "[I]: Benchmark for MobileNetV3 Small - TRT FP16 finished\n",
      "[I]: Adding model stat to model_stats.json\n"
     ]
    }
   ],
   "source": [
    "# Quantized models ran through TensorRT engine\n",
    "from Quantization.benchmark_trt import benchmark_model_trt\n",
    "\n",
    "json_path = \"model_stats.json\"\n",
    "\n",
    "dataloader = build_dataloader(\n",
    "    train_path = \"Data/mitbih_mif_train_small.h5\",\n",
    "    test_path  = \"Data/mitbih_mif_test.h5\",\n",
    "    batch_size = 1,\n",
    "    transform  = Resize((152, 152), antialias=None)\n",
    ")\n",
    "\n",
    "models = {\n",
    "    \"resnet18\": [\n",
    "        (\"ResNet18 - TRT FP32\", \"fp32\"),\n",
    "        (\"ResNet18 - TRT FP16\", \"fp16\"),\n",
    "    ],\n",
    "    \"mobilenet_v3_small\": [\n",
    "        (\"MobileNetV3 Small - TRT FP32\", \"fp32\"),\n",
    "        (\"MobileNetV3 Small - TRT FP16\", \"fp16\"),\n",
    "    ]\n",
    "}\n",
    "\n",
    "for model, precisions in models.items():\n",
    "    for name, precision in precisions:\n",
    "        engine_path = f\"Quantization/Engine/{model}_ecg_ep50_i152_{precision}.engine\"\n",
    "        stat = benchmark_model_trt(engine_path, dataloader[\"test\"], name)\n",
    "        add_model_stat_to_json(json_path, stat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Pruned VGG\n",
    "\n",
    "from Quantization.benchmark_trt import benchmark_model_trt\n",
    "json_path = \"model_stats.json\"\n",
    "\n",
    "dataloader = build_dataloader(\n",
    "    train_path = \"Data/mitbih_mif_train_small.h5\",\n",
    "    test_path  = \"Data/mitbih_mif_test.h5\",\n",
    "    batch_size = 1,\n",
    "    transform  = Resize((152, 152), antialias=None)\n",
    ")\n",
    "\n",
    "engine_path = f\"Quantization/Engine/vgg16_bn_custom_ecg_ep30_i152_p0.8_fp16.engine\"\n",
    "stat = benchmark_model_trt(engine_path, dataloader[\"test\"], \"VGG16 Custom 152 - Layer Pruned 0.8 - TRT FP16\")\n",
    "add_model_stat_to_json(json_path, stat)\n",
    "\n",
    "engine_path = f\"Quantization/Engine/vgg16_bn_custom_ecg_ep30_i152_p0.8_int8.engine\"\n",
    "stat = benchmark_model_trt(engine_path, dataloader[\"test\"], \"VGG16 Custom 152 - Layer Pruned 0.8 - TRT INT8\")\n",
    "add_model_stat_to_json(json_path, stat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[I]: Benchmarking model ResNet18 - FP16\n",
      "[I]: \tGetting model params and MACs\n",
      "[I]: \tLatency measurement skipped\n",
      "[I]: \tEvaluating\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                       \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[I]: Benchmarking for ResNet18 - FP16 finished\n",
      "[I]: Adding model stat to model_stats.json\n",
      "[I]: Benchmarking model MobileNetV3 Small - FP16\n",
      "[I]: \tGetting model params and MACs\n",
      "[I]: \tLatency measurement skipped\n",
      "[I]: \tEvaluating\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                       "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[I]: Benchmarking for MobileNetV3 Small - FP16 finished\n",
      "[I]: Adding model stat to model_stats.json\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r"
     ]
    }
   ],
   "source": [
    "# Half precision models without TensorRT\n",
    "\n",
    "json_path = \"model_stats.json\"\n",
    "\n",
    "dataloader = build_dataloader(\n",
    "    train_path     = \"Data/mitbih_mif_train_small.h5\",\n",
    "    test_path      = \"Data/mitbih_mif_test.h5\",\n",
    "    batch_size     = 128,\n",
    "    transform      = Resize((152, 152), antialias=None),\n",
    "    half_precision = True\n",
    ")\n",
    "\n",
    "models = {\n",
    "    \"resnet18\":           \"ResNet18 - FP16\", \n",
    "    \"mobilenet_v3_small\": \"MobileNetV3 Small - FP16\", \n",
    "}\n",
    "\n",
    "for model, name in models.items():\n",
    "    loaded_model = load_model_from_pretrained(model, f\"Pretrained/{model}_ecg_ep50_i152.pth\", 5)\n",
    "    stat = benchmark_model(loaded_model.to(torch.float16), dataloader[\"test\"], name, no_latency=True)\n",
    "    add_model_stat_to_json(json_path, stat)\n",
    "    del loaded_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ModelStats(name='ResNet18 - FP16', macs=880630784.0, params=11179077, latency=0.0034244098901748655, accuracy=98.38754272460938)\n",
      "ModelStats(name='MobileNetV3 Small - FP16', macs=30815864.0, params=1522981, latency=0.007043296384811401, accuracy=97.92617797851562)\n",
      "[I]: Adding model stat to model_stats.json\n",
      "[I]: Adding model stat to model_stats.json\n"
     ]
    }
   ],
   "source": [
    "# GPU latency without TensorRT\n",
    "json_path = \"model_stats.json\"\n",
    "\n",
    "\n",
    "# Half precision. For now, manually replace the latency in the json file\n",
    "models = {\n",
    "    \"resnet18\":           \"ResNet18 - FP16\", \n",
    "    \"mobilenet_v3_small\": \"MobileNetV3 Small - FP16\", \n",
    "}\n",
    "\n",
    "for model, name in models.items():\n",
    "    loaded_model = load_model_from_pretrained(model, f\"Pretrained/{model}_ecg_ep50_i152.pth\", 5)\n",
    "    model_stat   = get_model_stats_from_json(json_path, [name])[0]\n",
    "\n",
    "    dummy_input  = torch.rand((1, 3, 152, 152)).to(torch.float16)\n",
    "\n",
    "    latencies = []\n",
    "    for _ in range(0, 10):\n",
    "        latencies.append(measure_latency(loaded_model.to(torch.float16), dummy_input, test_device=device))\n",
    "    model_stat.latency = sum(latencies) / len(latencies)\n",
    "    print(model_stat)\n",
    "\n",
    "    del loaded_model\n",
    "\n",
    "\n",
    "# Full precision\n",
    "models = {\n",
    "    \"resnet18\":           \"ResNet18\", \n",
    "    \"mobilenet_v3_small\": \"MobileNetV3 Small\", \n",
    "    \"vgg16_bn_custom\":    \"VGG16 Custom 152\"\n",
    "}\n",
    "\n",
    "for model, name in models.items():\n",
    "    loaded_model = load_model_from_pretrained(model, f\"Pretrained/{model}_ecg_ep50_i152.pth\", 5)\n",
    "    model_stat   = get_model_stats_from_json(json_path, [name])[0]\n",
    "\n",
    "    dummy_input  = torch.rand((1, 3, 152, 152))\n",
    "\n",
    "    latencies = []\n",
    "    for _ in range(0, 10):\n",
    "        latencies.append(measure_latency(loaded_model, dummy_input, test_device=device))\n",
    "    model_stat.latency = sum(latencies) / len(latencies)\n",
    "\n",
    "    model_stat.name = f\"{name} - FP32\"\n",
    "    add_model_stat_to_json(json_path, model_stat)\n",
    "\n",
    "    del loaded_model\n",
    "\n",
    "# Pruned VGG custom\n",
    "models = {\n",
    "    \"vgg16_bn_custom\": \"VGG16 Custom 152 - Layer Pruned 0.8\"\n",
    "}\n",
    "\n",
    "for model, name in models.items():\n",
    "    loaded_model = load_vgg_custom_from_pruned(f\"Pretrained/VGG-Pruned/{model}_ecg_ep30_i152_p0.8.pth\", 0.8, torch.rand((1, 3, 152, 152)), 5)\n",
    "    model_stat   = get_model_stats_from_json(json_path, [name])[0]\n",
    "\n",
    "    dummy_input  = torch.rand((1, 3, 152, 152))\n",
    "\n",
    "    latencies = []\n",
    "    for _ in range(0, 10):\n",
    "        latencies.append(measure_latency(loaded_model, dummy_input, test_device=device))\n",
    "    model_stat.latency = sum(latencies) / len(latencies)\n",
    "\n",
    "    model_stat.name = f\"{name} - FP32\"\n",
    "    add_model_stat_to_json(json_path, model_stat)\n",
    "\n",
    "    del loaded_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[I]: Benchmarking model VGG16 - Resolution 224\n",
      "[I]: \tGetting model params and MACs\n",
      "[I]: \tMeasuring latency\n",
      "[I]: \tEvaluating\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                       \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[I]: Benchmarking for VGG16 - Resolution 224 finished\n",
      "[I]: Adding model stat to model_stats.json\n",
      "[I]: Adding model stat to model_stats.json\n"
     ]
    }
   ],
   "source": [
    "# Full precision VGG 224\n",
    "json_path = \"model_stats.json\"\n",
    "\n",
    "dataloader = build_dataloader(\n",
    "    train_path     = \"Data/mitbih_mif_train_small.h5\",\n",
    "    test_path      = \"Data/mitbih_mif_test.h5\",\n",
    "    batch_size     = 32,\n",
    "    transform      = Resize((224, 224), antialias=None),\n",
    ")\n",
    "\n",
    "# Benchmark\n",
    "loaded_model = load_model_from_pretrained(\"vgg16_bn\", f\"Pretrained/vgg16_bn_ecg_ep50_i224.pth\", 5)\n",
    "model_stat   = benchmark_model(loaded_model, dataloader[\"test\"], \"VGG16 - Resolution 224\")\n",
    "add_model_stat_to_json(json_path, model_stat)\n",
    "\n",
    "# GPU Latency\n",
    "model_stat  = get_model_stats_from_json(json_path, [\"VGG16 - Resolution 224\"])[0]\n",
    "dummy_input = torch.rand((1, 3, 224, 224))\n",
    "\n",
    "latencies = []\n",
    "for _ in range(0, 10):\n",
    "    latencies.append(measure_latency(loaded_model, dummy_input, test_device=device))\n",
    "model_stat.latency = sum(latencies) / len(latencies)\n",
    "\n",
    "model_stat.name = f\"VGG16 - Resolution 224 - FP32\"\n",
    "add_model_stat_to_json(json_path, model_stat)\n",
    "\n",
    "del loaded_model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Size of Training Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Size of training data\n",
    "\n",
    "original_data = get_dataset_size(image_size=187, channels=3, num_images=87554, data_width=32)\n",
    "paper_data    = get_dataset_size(image_size=227, channels=3, num_images=152471, data_width=32)\n",
    "my_data       = get_dataset_size(image_size=152, channels=3, num_images=27554, data_width=32)\n",
    "\n",
    "compare_single_values(\n",
    "    [original_data, paper_data, my_data],\n",
    "    [\"Original\", \"Augmented\", \"Ours\"],\n",
    "    axis  = \"Size (GB)\",\n",
    "    title = \"Size of Training Dataset\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1500x400 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1500x400 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "models = {\n",
    "    \"AlexNet\":           \"ALX\",\n",
    "    \"ResNet18\":          \"RSN\",\n",
    "    \"VGG16\":             \"VGG\", \n",
    "    \"MobileNetV3 Large\": \"MBL\", \n",
    "    \"MobileNetV3 Small\": \"MBS\", \n",
    "    \"OFA 595M\":          \"OFA\", \n",
    "    \"OFA Pixel 1 20ms\":  \"OP1\"\n",
    "}\n",
    "\n",
    "stats = get_model_stats_from_json(\"model_stats.json\", models)\n",
    "for stat in stats:\n",
    "    stat.name = models[stat.name]\n",
    "\n",
    "compare_models(stats, fig_size=(15, 4))\n",
    "compare_models([stats[1], stats[3], stats[4], stats[5], stats[6]], fig_size=(15, 4))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Resolution Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Best Accuracy and Latency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "models = {\n",
    "    \"MobileNetV3 Small - Resolution 80\":  \"80\",\n",
    "    \"MobileNetV3 Small - Resolution 104\": \"104\",\n",
    "    \"MobileNetV3 Small - Resolution 128\": \"128\", \n",
    "    \"MobileNetV3 Small - Resolution 152\": \"152\", \n",
    "    \"MobileNetV3 Small - Resolution 176\": \"176\", \n",
    "    \"MobileNetV3 Small - Resolution 200\": \"200\", \n",
    "    \"MobileNetV3 Small - Resolution 224\": \"224\"\n",
    "}\n",
    "\n",
    "stats = get_model_stats_from_json(\"model_stats.json\", models)\n",
    "for stat in stats:\n",
    "    stat.name = models[stat.name]\n",
    "\n",
    "compare_models(stats, show_macs=False, show_params=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Running Accuracy and Loss"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Running data can be collected by training the model and recording the loss-per-batch and accuracy-per-epoch."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "running_accuracy_raw = np.loadtxt(\"running_acc_2.txt\")\n",
    "running_loss_raw     = np.loadtxt(\"running_loss_2.txt\")\n",
    "running_accuracy     = dict()\n",
    "running_loss         = dict()\n",
    "for i, accs in enumerate(running_accuracy_raw):\n",
    "    running_accuracy[str(resolutions[i])] = accs\n",
    "\n",
    "running_loss[80]  = running_loss_raw[0]\n",
    "running_loss[152] = running_loss_raw[3]\n",
    "running_loss[224] = running_loss_raw[6]\n",
    "\n",
    "compare_list_values(\n",
    "    running_accuracy,\n",
    "    y_axis  = \"Accuracy\",\n",
    "    x_axis  = \"Epoch\",\n",
    "    title   = \"Resolution Sensitivity of MobileNetV3 Small - Accuracy\",\n",
    "    y_range = (80, 100),\n",
    ")\n",
    "compare_list_values(\n",
    "    running_loss,\n",
    "    y_axis = \"Loss\",\n",
    "    x_axis = \"Steps\",\n",
    "    title  = \"Resolution Sensitivity of MobileNetV3 Small - Loss\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## VGG Pruning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Per-layer Sparsity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1500x400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "models = {\n",
    "    \"VGG16\":                     \"Base\",\n",
    "    \"VGG16 - Layer Pruned 0.5\":  \"0.5\",\n",
    "    \"VGG16 - Layer Pruned 0.6\":  \"0.6\",\n",
    "    \"VGG16 - Layer Pruned 0.7\":  \"0.7\",\n",
    "    \"VGG16 - Layer Pruned 0.8\":  \"0.8\",\n",
    "    \"VGG16 - Layer Pruned 0.9\":  \"0.9\",\n",
    "}\n",
    "\n",
    "stats = get_model_stats_from_json(\"model_stats.json\", models)\n",
    "for stat in stats:\n",
    "    stat.name = models[stat.name]\n",
    "\n",
    "compare_models(stats, show_macs=False, fig_size=(15, 4))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Global Sparsity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1500x400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "models = {\n",
    "    \"VGG16\":                      \"Base\",\n",
    "    \"VGG16 - Global Pruned 0.5\":  \"0.5\",\n",
    "    \"VGG16 - Global Pruned 0.6\":  \"0.6\",\n",
    "    \"VGG16 - Global Pruned 0.7\":  \"0.7\",\n",
    "    \"VGG16 - Global Pruned 0.8\":  \"0.8\",\n",
    "    \"VGG16 - Global Pruned 0.9\":  \"0.9\",\n",
    "}\n",
    "\n",
    "stats = get_model_stats_from_json(\"model_stats.json\", models)\n",
    "for stat in stats:\n",
    "    stat.name = models[stat.name]\n",
    "\n",
    "compare_models(stats, show_macs=False, fig_size=(15, 4))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Per-layer vs Global"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_layer_vs_global(stats):\n",
    "    names   = [\"0.5\", \"0.6\", \"0.7\", \"0.8\", \"0.9\"]\n",
    "    accs    = [(stats[i].accuracy, stats[i+1].accuracy) for i in range(0, len(stats), 2)]\n",
    "    latency = [(stats[i].latency, stats[i+1].latency) for i in range(0, len(stats), 2)]\n",
    "\n",
    "    colors = sns.color_palette(\"husl\", 2)\n",
    "\n",
    "    _, ax = plt.subplots(1, 2, figsize=(15, 4))\n",
    "    current_bar = 0\n",
    "    for group in accs:\n",
    "        ax[0].bar(current_bar, group[0], color=colors[0])\n",
    "        current_bar += 1\n",
    "\n",
    "        ax[0].bar(current_bar, group[1], color=colors[1])\n",
    "        current_bar += 2\n",
    "\n",
    "    current_bar = 0\n",
    "    for group in latency:\n",
    "        ax[1].bar(current_bar, round(group[0] * 1000, 1), color=colors[0])\n",
    "        current_bar += 1\n",
    "\n",
    "        ax[1].bar(current_bar, round(group[1] * 1000, 1), color=colors[1])\n",
    "        current_bar += 2\n",
    "\n",
    "    ax[0].set_ylim([97, 100])\n",
    "    ax[0].set_xticks([0.5, 3.5, 6.5, 9.5, 12.5])\n",
    "    ax[0].set_xticklabels(names)\n",
    "    ax[0].set_title(\"Accuracy\")\n",
    "    ax[0].set_xlabel(\"Sparsity\")\n",
    "    ax[0].legend([\"Layer\", \"Global\"])\n",
    "\n",
    "    ax[1].set_xticks([0.5, 3.5, 6.5, 9.5, 12.5])\n",
    "    ax[1].set_xticklabels(names)\n",
    "    ax[1].set_title(\"Latency (ms)\")\n",
    "    ax[1].set_xlabel(\"Sparsity\")\n",
    "    ax[1].legend([\"Layer\", \"Global\"])\n",
    "\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1500x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "models = {\n",
    "    \"VGG16 - Layer Pruned 0.5\":  \"0.5\",\n",
    "    \"VGG16 - Global Pruned 0.5\": \"0.5\",\n",
    "    \"VGG16 - Layer Pruned 0.6\":  \"0.6\",\n",
    "    \"VGG16 - Global Pruned 0.6\": \"0.6\",\n",
    "    \"VGG16 - Layer Pruned 0.7\":  \"0.7\",\n",
    "    \"VGG16 - Global Pruned 0.7\": \"0.7\",\n",
    "    \"VGG16 - Layer Pruned 0.8\":  \"0.8\",\n",
    "    \"VGG16 - Global Pruned 0.8\": \"0.8\",\n",
    "    \"VGG16 - Layer Pruned 0.9\":  \"0.9\",\n",
    "    \"VGG16 - Global Pruned 0.9\": \"0.9\",\n",
    "}\n",
    "\n",
    "stats = get_model_stats_from_json(\"model_stats.json\", models)\n",
    "for stat in stats:\n",
    "    stat.name = models[stat.name]\n",
    "\n",
    "plot_layer_vs_global(stats)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Quantization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1500x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Base - TRT16: 0.009139654994896773\n",
      "Latency reduction: 8.469286766104993\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1500x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Base - TRT16: 0.036540941196236076\n",
      "Latency reduction: 5.059836883463307\n"
     ]
    }
   ],
   "source": [
    "def plot_base_vs_trt(stats, accuracy_range, model):\n",
    "    names   = [\"FP32\", \"FP16\"]\n",
    "    accs    = [(stats[i].accuracy, stats[i+1].accuracy) for i in range(0, len(stats), 2)]\n",
    "    latency = [(stats[i].latency, stats[i+1].latency) for i in range(0, len(stats), 2)]\n",
    "\n",
    "    colors = sns.color_palette(\"husl\", 2)\n",
    "\n",
    "    _, ax = plt.subplots(1, 2, figsize=(15, 4))\n",
    "    current_bar = 0\n",
    "    for group in accs:\n",
    "        ax[0].bar(current_bar, group[0], color=colors[0])\n",
    "        current_bar += 1\n",
    "\n",
    "        ax[0].bar(current_bar, group[1], color=colors[1])\n",
    "        current_bar += 2\n",
    "\n",
    "    current_bar = 0\n",
    "    for group in latency:\n",
    "        ax[1].bar(current_bar, round(group[0] * 1000, 1), color=colors[0])\n",
    "        current_bar += 1\n",
    "\n",
    "        ax[1].bar(current_bar, round(group[1] * 1000, 1), color=colors[1])\n",
    "        current_bar += 2\n",
    "\n",
    "    ax[0].set_ylim(accuracy_range)\n",
    "    ax[0].set_xticks([0.5, 3.5])\n",
    "    ax[0].set_xticklabels(names)\n",
    "    ax[0].set_title(f\"Accuracy - {model}\")\n",
    "    ax[0].set_xlabel(\"Precision\")\n",
    "    ax[0].legend([\"PyTorch\", \"TRT\"])\n",
    "\n",
    "    ax[1].set_xticks([0.5, 3.5])\n",
    "    ax[1].set_xticklabels(names)\n",
    "    ax[1].set_title(f\"Latency (ms) - {model}\")\n",
    "    ax[1].set_xlabel(\"Precision\")\n",
    "    ax[1].legend([\"PyTorch\", \"TRT\"])\n",
    "\n",
    "    plt.show()\n",
    "\n",
    "resnet_models = [\n",
    "    \"ResNet18 - FP32\", \"ResNet18 - TRT FP32\",\n",
    "    \"ResNet18 - FP16\", \"ResNet18 - TRT FP16\"\n",
    "]\n",
    "stats = get_model_stats_from_json(\"model_stats.json\", resnet_models, sort=True)\n",
    "plot_base_vs_trt(stats, (98, 98.5), \"ResNet18\")\n",
    "print(f\"Base - TRT16: {stats[0].accuracy - stats[3].accuracy}\")\n",
    "print(f\"Latency reduction: {stats[0].latency / stats[3].latency}\")\n",
    "\n",
    "mobile_models = [\n",
    "    \"MobileNetV3 Small - FP32\", \"MobileNetV3 Small - TRT FP32\",\n",
    "    \"MobileNetV3 Small - FP16\", \"MobileNetV3 Small - TRT FP16\",\n",
    "]\n",
    "stats = get_model_stats_from_json(\"model_stats.json\", mobile_models, sort=True)\n",
    "plot_base_vs_trt(stats, (97, 98.2), \"MobileNetV3 Small\")\n",
    "print(f\"Base - TRT16: {stats[0].accuracy - stats[3].accuracy}\")\n",
    "print(f\"Latency reduction: {stats[0].latency / stats[3].latency}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## VGG Quantization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### VGG Resolution Changes for ONNX Export"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Resolution 224\n",
      "Top:  98.63\n",
      "Mean: 98.31\n",
      "Std:  0.46\n",
      "Resolution 152\n",
      "Top:  98.78\n",
      "Mean: 98.41\n",
      "Std:  0.22\n"
     ]
    }
   ],
   "source": [
    "def compute_top_mean_std(values):\n",
    "    top  = max(values)\n",
    "    mean = round(np.mean(values[20:]), 2)\n",
    "    std  = round(np.std(values[20:]), 2)\n",
    "    print(f\"Top:  {top}\\nMean: {mean}\\nStd:  {std}\")\n",
    "\n",
    "running_accuracy_224 = np.loadtxt(\"Running-Data/vgg_acc_224.txt\")\n",
    "running_accuracy_152 = np.loadtxt(\"Running-Data/vgg_acc_152.txt\")\n",
    "\n",
    "print(\"Resolution 224\")\n",
    "compute_top_mean_std(running_accuracy_224) \n",
    "\n",
    "print(\"Resolution 152\")\n",
    "compute_top_mean_std(running_accuracy_152) \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Main Quantization Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 500x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 500x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 500x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Size of 224 B: 512.2737007141113\n",
      "Size of P16: 4.956932067871094\n",
      "Size of P8: 2.478466033935547\n",
      "\n",
      "Reductions 224 B => P16\n",
      "\tAccuracy: 0.37 (99.62%)\n",
      "\tLatency:  0.00 (14.93x)\n",
      "\tParams:   131690617.00 (51.67x)\n",
      "\tSize:     507.32 (103.34x)\n",
      "\n",
      "Reductions 224 B => P8\n",
      "\tAccuracy: 3.08 (96.88%)\n",
      "\tLatency:  0.00 (15.96x)\n",
      "\tParams:   131690617.00 (51.67x)\n",
      "\tSize:     509.80 (206.69x)\n",
      "\n",
      "Name:     224 B\n",
      "Accuracy: 98.63%\n",
      "Latency:  3.6 ms\n",
      "Params:   134 M\n",
      "MACs:     15520 M\n",
      "\n",
      "Name:     152 B\n",
      "Accuracy: 98.78%\n",
      "Latency:  2.5 ms\n",
      "Params:   65 M\n",
      "MACs:     7076 M\n",
      "\n",
      "Name:     P32\n",
      "Accuracy: 98.26%\n",
      "Latency:  2.4 ms\n",
      "Params:   3 M\n",
      "MACs:     284 M\n",
      "\n",
      "Name:     P16\n",
      "Accuracy: 98.26%\n",
      "Latency:  0.2 ms\n",
      "Params:   3 M\n",
      "MACs:     284 M\n",
      "\n",
      "Name:     P8\n",
      "Accuracy: 95.55%\n",
      "Latency:  0.2 ms\n",
      "Params:   3 M\n",
      "MACs:     284 M\n",
      "\n"
     ]
    }
   ],
   "source": [
    "models = {\n",
    "    \"VGG16 - Resolution 224 - FP32\":                  \"224 B\",\n",
    "    \"VGG16 Custom 152 - FP32\":                        \"152 B\",\n",
    "    \"VGG16 Custom 152 - Layer Pruned 0.8 - FP32\":     \"P32\",\n",
    "    \"VGG16 Custom 152 - Layer Pruned 0.8 - TRT FP16\": \"P16\",\n",
    "    \"VGG16 Custom 152 - Layer Pruned 0.8 - TRT INT8\": \"P8\",\n",
    "}\n",
    "\n",
    "stats = get_model_stats_from_json(\"model_stats.json\", models, sort=True)\n",
    "for stat in stats:\n",
    "    stat.name = models[stat.name]\n",
    "\n",
    "# Accuracy vs Latency\n",
    "accuracy_vs_latency = {\n",
    "    stat.name: [(round(stat.latency * 1000, 1), stat.accuracy)] for stat in stats\n",
    "}\n",
    "config = PlotConfig(\n",
    "    title    = \"VGG Optimization - Accuracy vs Latency\",\n",
    "    x_label  = \"Latency (ms)\",\n",
    "    y_label  = \"Accuracy\",\n",
    "    y_range  = (95, 100),\n",
    "    fig_size = (5, 4)\n",
    ")\n",
    "compare_pairwise(accuracy_vs_latency, config)\n",
    "\n",
    "# Parameters\n",
    "parameters = {\n",
    "    stat.name: round(stat.params / 1e6, 1) for stat in stats\n",
    "}\n",
    "config = PlotConfig(\n",
    "    title    = \"VGG Optimization - Parameter Count\",\n",
    "    y_label  = \"Parameters (M)\",\n",
    "    y_range  = (0, 150),\n",
    "    x_grid   = False,\n",
    "    fig_size = (5, 4)\n",
    ")\n",
    "compare_single_values(parameters, config, horizontal=False)\n",
    "\n",
    "# Size\n",
    "sizes = {\n",
    "    stat.name: stat.params * 32 / MiB for stat in stats\n",
    "}\n",
    "sizes[\"P16\"] = sizes[\"P32\"] / 2\n",
    "sizes[\"P8\"] = sizes[\"P32\"] / 4\n",
    "\n",
    "for name, size in sizes.items():\n",
    "    sizes[name] = round(size, 2)\n",
    "\n",
    "config = PlotConfig(\n",
    "    title    = \"VGG Optimization - Model Size\",\n",
    "    y_label  = \"Model Size (MiB)\",\n",
    "    y_range  = (0, 600),\n",
    "    x_grid   = False,\n",
    "    fig_size = (5, 4)\n",
    ")\n",
    "compare_single_values(sizes, config, horizontal=False)\n",
    "\n",
    "# Reductions\n",
    "parameters = {stat.name: stat.params for stat in stats}\n",
    "accuracy   = {stat.name: stat.accuracy for stat in stats}\n",
    "latency   = {stat.name: stat.latency for stat in stats}\n",
    "\n",
    "size_224B  = parameters[\"224 B\"] * 32 / MiB\n",
    "size_P16   = parameters[\"P32\"] * 16 / MiB\n",
    "size_P8    = parameters[\"P32\"] * 8 / MiB\n",
    "print(f\"Size of 224 B: {size_224B}\")\n",
    "print(f\"Size of P16: {size_P16}\")\n",
    "print(f\"Size of P8: {size_P8}\")\n",
    "print()\n",
    "\n",
    "precisions = [\"P16\", \"P8\"]\n",
    "for pr in precisions:\n",
    "    accuracy_difference = accuracy['224 B'] - accuracy[pr]\n",
    "    accuracy_maintained = accuracy[pr] / accuracy['224 B'] * 100\n",
    "    latency_difference  = latency['224 B'] - latency[pr]\n",
    "    latency_reduction   = latency['224 B'] / latency[pr]\n",
    "    param_difference    = parameters['224 B'] - parameters[pr]\n",
    "    param_reduction     = parameters['224 B'] / parameters[pr]\n",
    "\n",
    "    size = size_P16 if pr == \"P16\" else size_P8\n",
    "    size_difference     = size_224B - size\n",
    "    size_reduction      = size_224B / size\n",
    "\n",
    "    print(f\"Reductions 224 B => {pr}\")\n",
    "    print(f\"\\tAccuracy: {accuracy_difference:.2f} ({accuracy_maintained:.2f}%)\")\n",
    "    print(f\"\\tLatency:  {latency_difference:.2f} ({latency_reduction:.2f}x)\")\n",
    "    print(f\"\\tParams:   {param_difference:.2f} ({param_reduction:.2f}x)\")\n",
    "    print(f\"\\tSize:     {size_difference:.2f} ({size_reduction:.2f}x)\")\n",
    "    print()\n",
    "\n",
    "# All stats\n",
    "for stat in stats:\n",
    "    display_model_stats(stat)\n",
    "    print()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "machine_learning",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
